A short review of clustering techniques

Data mining has been applied successfully in various research area and takes an important role in the business domain. This paper examines the several clustering techniques based on the basis of cluster policy and method, and exhibits the steps for clustering process. The paper discusses some of the important concepts regarding data type, feature selection, and cluster evolution. The results indicate that overall clustering techniques can be divided into the seven groups, namely Distance based, Density based, Model based, Grid based, Kernel based, Spectral based, Hierarchical based. This paper will serves as a guideline for industry and academic world.